|
|
import streamlit as st
|
|
|
import tensorflow as tf
|
|
|
from tensorflow.keras.models import load_model
|
|
|
from PIL import Image
|
|
|
import numpy as np
|
|
|
import os
|
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
|
|
MODEL_NAME = "pneumonia_detection_model.keras"
|
|
|
|
|
|
HF_REPO_ID = "saad1BM/pneumonia-detection-system"
|
|
|
CLASS_NAMES = ['NORMAL', 'PNEUMONIA']
|
|
|
IMAGE_SIZE = (224, 224)
|
|
|
|
|
|
@st.cache_resource
|
|
|
def load_pneumonia_model():
|
|
|
|
|
|
model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=MODEL_NAME)
|
|
|
|
|
|
model = load_model(model_path)
|
|
|
return model
|
|
|
|
|
|
|
|
|
st.set_page_config(page_title="Pneumonia Detection System (AI Powered)", layout="centered")
|
|
|
|
|
|
st.title("🫁 Pneumonia Detection System (AI Powered)")
|
|
|
st.caption("Upload a chest X-ray image to predict Normal or Pneumonia.")
|
|
|
|
|
|
|
|
|
try:
|
|
|
model = load_pneumonia_model()
|
|
|
except Exception as e:
|
|
|
st.error(f"Error loading model from Hugging Face: {e}")
|
|
|
st.info("Please make sure the model file is correctly uploaded to Hugging Face Hub.")
|
|
|
st.stop()
|
|
|
|
|
|
|
|
|
uploaded_file = st.file_uploader("Choose a Chest X-ray Image...", type=["jpg", "jpeg", "png"])
|
|
|
|
|
|
if uploaded_file is not None:
|
|
|
|
|
|
image = Image.open(uploaded_file).convert("RGB")
|
|
|
st.image(image, caption='Uploaded X-ray Image', use_column_width=True)
|
|
|
|
|
|
|
|
|
if st.button("Detect Pneumonia"):
|
|
|
st.subheader("📊 Prediction Result")
|
|
|
|
|
|
with st.spinner('Analyzing X-ray image...'):
|
|
|
|
|
|
img = image.resize(IMAGE_SIZE)
|
|
|
img_array = np.array(img) / 255.0
|
|
|
img_array = np.expand_dims(img_array, axis=0)
|
|
|
|
|
|
|
|
|
predictions = model.predict(img_array)
|
|
|
score = tf.nn.softmax(predictions[0])
|
|
|
|
|
|
|
|
|
predicted_class_index = np.argmax(score)
|
|
|
predicted_class = CLASS_NAMES[predicted_class_index]
|
|
|
confidence = np.max(score) * 100
|
|
|
|
|
|
if predicted_class == 'PNEUMONIA':
|
|
|
st.error(f"### ⚠️ Prediction: {predicted_class}")
|
|
|
st.markdown(f"**Confidence:** **{confidence:.2f}%**")
|
|
|
|
|
|
else:
|
|
|
st.success(f"### ✅ Prediction: {predicted_class}")
|
|
|
st.markdown(f"**Confidence:** **{confidence:.2f}%**")
|
|
|
|
|
|
|
|
|
st.markdown("---")
|
|
|
st.bar_chart({
|
|
|
"Normal": score[0].numpy(),
|
|
|
"Pneumonia": score[1].numpy()
|
|
|
}) |
